SUMMARYPurpose: High-frequency oscillations (HFOs) known as ripples and fast ripples (250-500 Hz) can be recorded from macroelectrodes inserted in patients with intractable focal epilepsy. They are most likely linked to epileptogenesis and have been found in the seizure onset zone (SOZ) of human ictal and interictal recordings. HFOs occur frequently at the time of interictal spikes, but were also found independently. This study analyses the relationship between spikes and HFOs and the occurrence of HFOs in nonspiking channels. Methods: Intracerebral EEGs of 10 patients with intractable focal epilepsy were studied using macroelectrodes. Rates of HFOs within and outside spikes, the overlap between events, event durations, and the percentage of spikes carrying HFOs were calculated and compared according to anatomical localization, spiking activity, and relationship to the SOZ.Results: HFOs were found in all patients, significantly more within mesial temporal lobe structures than in neocortex. HFOs could be seen in spiking as well as nonspiking channels in all structures. Rates and durations of HFOs were significantly higher in the SOZ than outside. It was possible to establish a rate of HFOs to identify the SOZ with better sensitivity and specificity than with the rate of spikes. Discussion: HFOs occurred to a large extent independently of spikes. They are most frequent in mesial temporal structures. They are prominent in the SOZ and provide additional information on epileptogenicity independently of spikes. It was possible to identify the SOZ with a high specificity by looking at only 10 min of HFO activity.

Interictal fast oscillations between 100 and 500 Hz have been reported in signals recorded from implanted microelectrodes in epileptic patients and experimental rat models. Oscillations between 250 and 500 Hz, or fast ripples (FR), appeared related to the epileptic focus whereas ripples (80-200 Hz) were not. We report high-frequency oscillations recorded with intracranial macroelectrodes in seven patients with refractory focal epilepsy during slow-wave sleep. We characterize the relation of fast oscillations to the seizure focus and quantify their concordance with epileptiform transients, with which they are strongly associated. The patients were selected because interictal spikes were found within and outside the seizure onset zone. Visual inspection was used to identify and classify the ripples and FRs according to their relation to epileptiform spikes. Continuous-time wavelet analysis was used to compute their power. Ripples were present in all patients while FRs where found in five of the seven patients. Most ripples and FRs occurred at the same time as epileptiform transients. The rate of occurrence of ripples was higher within the seizure onset zone than outside in four of seven patients. The rate of FRs was much higher within the seizure onset zone than outside in four of the five patients with FRs (in these four patients, FRs were almost inexistent outside the seizure onset zone). The power of ripples and FRs tended to be higher in the electrodes where their rate was also higher. These results indicate that FRs were more restricted to the electrodes located within the seizure onset zone, especially to the hippocampus, than ripples. In only one patient, FRs were more frequent outside the seizure onset zone; this patient was the only one with cortical dysplasia and the electrode with a high rate of FRs was inside the lesion. This study demonstrates that interictal ripples and FRs can be recorded with depth macroelectrodes in patients. Most occur at the time of epileptiform spikes but some are isolated. Ripples do not show a clear differentiation between the seizure onset zone and remote areas, whereas FRs have a higher rate and higher power in the seizure onset zone. Our results also suggest a special capacity of the abnormal hippocampus to generate FRs, although they were also recorded in other structures.

SUMMARYPurpose: Intracranial depth macroelectrode recordings from patients with focal seizures demonstrate interictal and ictal high frequency oscillations (HFOs, 80-500 Hz). These HFOs are more frequent in the seizure-onset zone (SOZ) and reported to be linked to seizure genesis. We evaluated whether HFO activity changes in a systematic way during the preictal period. Methods: Fifteen minutes of preictal intracranial electroencephalography (EEG) recordings were evaluated in seven consecutive patients with welldefined SOZ. EEG was filtered at 500 Hz and sampled at 2,000 Hz. Ripples (80-250 Hz) and fast ripples (250-500 Hz) were visually marked, and spectral analysis was performed in seizure-onset as well as nonseizure-onset channels. Linear regressions fitted to the power trends corresponding to intervals of 1, 5, and 15 min before the seizure onset was calculated. Results: Total rates of HFOs were significantly higher in the SOZ than outside. Preictal increases and decreases in HFO rates and band power could be detected in all patients, and they were not limited to the SOZs. These measures were very variable, and no systematic trends were observed when comparing patients or seizures in the same patient. Discussion: High frequencies in the range of 80-500 Hz are present during the preictal period and are more prominent in the SOZ. They do not change in a systematic way before seizure onset for the horizons we tested. The 80-500 Hz band may be used for the localization of seizure-onset areas but may be more difficult to use for seizure prediction purposes.

High Frequency Oscillations (HFOs) in the EEG are a promising biomarker of epileptogenic tissue. Given that the visual marking of HFOs is highly time-consuming and subjective, automatic detectors are necessary. In this study, we present a novel automatic detector that detects HFOs by incorporating information of previously detected baselines. The detector was trained on 72 channels and tested on 278, achieving a mean sensitivity of 96.8% with a mean false positive rate of 4.86%. This low rate is reasonable since only visually marked baseline segments were considered as the true negatives. This detector could be useful for the systematic study of HFOs and for their eventual clinical application.

ArtiSynth is a modular, component-based system for performing dynamic 3D simulations of the human vocal tract and face. It provides a test bed for research in areas such as speech synthesis, linguistics, medicine, and dentistry. ArtiSynths framework enables researchers to construct, refine, and exchange models of all parts of the vocal tract and surrounding structures. ArtiSynth introduces a probe concept to unify input and output data flow, which allows control of and access to models with time varying data series. ArtiSynth supports interconnected heterogeneous models, such as rigid body, mass-spring, and parametric, using a point-set connection method, called markers, for constraint satisfaction. Using ArtiSynth, we created a muscle-driven rigid body jaw model, a parametric principle component tongue model from MRI images, a parametric lip model, and mass-spring face tissue model. We combined them in various ways. Data from medical imaging (MRI, CT, and ultrasound) and other technologies such as optical tracking can be used to drive ArtiSynth models. We are currently developing an acoustical rendering framework supporting source-filter models and other advanced methods. The system incorporates a powerful scripting interface as well as an easy-to-use graphical interface. [Work supported by NSERC Canada and ATR Japan.]

Leading edge technology advancements have posed big challenges for the digital design flow. Designing multimillion gate ICs at aggressive cycletimes requires new design methodologies and innovative approaches. Hierarchical analysis is a key need to achieve these cycletime goals and gain capacity and runtime advantages in the design flow. Timing and crosstalk delay closure are iterative and hierarchical analysis gains significant cycletime in the overall closure process. In this paper, we present a complete solution for true hierarchical timing and crosstalk delay signoff. The paper discusses challenging problems and caveats with hierarchical signoff and novel solutions to address them. We explore gaps in hierarchical STA and present the first structured solution for hierarchical signoff. The results show 2x-4x cycletime and memory savings using our hierarchical signoff analysis methodology.